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Food Science and Human Wellness 2022, 11(3): 524-536 https://doi.org/10.1016/j.fshw.2021.12.010
Review Article | Open Access | Issue | Published: 04 February 2022

Multi-omics approach in tea polyphenol research regarding tea plant growth, development and tea processing: current technologies and perspectives

Show Author's Information Jingwen Li,  Yu Wang,  Joon Hyuk Suh( )
Department of Food Science and Human Nutrition, Citrus Research and Education Center, University of Florida, FL 33850, USA
Keywords:
Multi-omics, Single-omics, Tea polyphenols, Tea breeding and processing
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Li J, Wang Y, Suh JH. Multi-omics approach in tea polyphenol research regarding tea plant growth, development and tea processing: current technologies and perspectives. Food Science and Human Wellness, 2022, 11(3): 524-536. https://doi.org/10.1016/j.fshw.2021.12.010
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Abstract Full text About this article

Polyphenols are one of the most important metabolites in tea due to their unique biological activities and health benefits, arousing great attention of researchers to investigate biochemical mechanisms of polyphenols during tea plant growth, development and tea processing. Although omics has been used as a major analytical platform for tea polyphenol research with some proven merits, a single-omics strategy remains a considerable challenge due to the complexity of biological system and functional processes of tea in each stage of tea production. Recent advances in multi-omics approaches and data analysis have enabled mining and mapping of enormous number of datasets at different biological scales from genotypes to phenotypes of living organisms. These new technologies combining genomics, metagenomics, transcriptomics, proteomics and/or metabolomics can pave a new avenue to address fundamental questions regarding polyphenol formation and changes in tea plants and products. Here, we review recent progresses in single- and multi-omics approaches that have been used in the field of tea polyphenol studies. The perspectives on future research and applications for improvement of tea polyphenols as well as current challenges of multi-omics studies for tea polyphenols are also discussed.


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About this article

Multi-omics approach in tea polyphenol research regarding tea plant growth, development and tea processing: current technologies and perspectives

Show Author's information Jingwen Li, Yu Wang, Joon Hyuk Suh( )
Department of Food Science and Human Nutrition, Citrus Research and Education Center, University of Florida, FL 33850, USA

Abstract

Polyphenols are one of the most important metabolites in tea due to their unique biological activities and health benefits, arousing great attention of researchers to investigate biochemical mechanisms of polyphenols during tea plant growth, development and tea processing. Although omics has been used as a major analytical platform for tea polyphenol research with some proven merits, a single-omics strategy remains a considerable challenge due to the complexity of biological system and functional processes of tea in each stage of tea production. Recent advances in multi-omics approaches and data analysis have enabled mining and mapping of enormous number of datasets at different biological scales from genotypes to phenotypes of living organisms. These new technologies combining genomics, metagenomics, transcriptomics, proteomics and/or metabolomics can pave a new avenue to address fundamental questions regarding polyphenol formation and changes in tea plants and products. Here, we review recent progresses in single- and multi-omics approaches that have been used in the field of tea polyphenol studies. The perspectives on future research and applications for improvement of tea polyphenols as well as current challenges of multi-omics studies for tea polyphenols are also discussed.

Keywords: Multi-omics, Single-omics, Tea polyphenols, Tea breeding and processing

References(134)

[1]

J. Hamilton-Miller, Antimicrobial properties of tea (Camellia sinensis L. ), Antimicrob. Agents Chemother. 39 (1995) 2375. https://doi.org/10.1128/aac.39.11.2375
DOI Google Scholar
[2]
H. Saberi, Tea: a global history, Reaktion Books 2010.
[3]

F. Zhao, H.T. Lin, S. Zhang, et al., Simultaneous determination of caffeine and some selected polyphenols in Wuyi Rock tea by high-performance liquid chromatography, J. Agric. Food Chem. 62 (2014) 2772-2781. https://doi.org/10.1021/jf4056314
DOI Google Scholar
[4]

Y. Wang, Z. Kan, H.J. Thompson, et al., Impact of six typical processing methods on the chemical composition of tea leaves using a single Camellia sinensis cultivar, Longjing 43, J. Agric. Food Chem. 67 (2019) 5423-5436. https://doi.org/10.1021/acs.jafc.8b05140
DOI Google Scholar
[5]

Z. Yan, Y. Zhong, Y. Duan, et al., Antioxidant mechanism of tea polyphenols and its impact on health benefits, Anim. Nutr. 6 (2020) 115-123. https://doi.org/10.1016/j.aninu.2020.01.001
DOI Google Scholar
[6]

X.Y. Xu, C.N. Zhao, S.Y. Cao, et al., Effects and mechanisms of tea for the prevention and management of cancers: an updated review, Crit. Rev. Food Sci. Nutr. 60 (2020) 1693-1705. https://doi.org/10.1080/10408398.2019.1588223
DOI Google Scholar
[7]

A. Forouzanfar, H.S. Mohammadipour, F. Forouzanfar, The potential role of tea in periodontal therapy: an updated review, Curr. Drug Discov. Technol. 18(1) (2020) 1-7. https://doi.org/10.2174/1389200221666200127114119
DOI Google Scholar
[8]

J.K. Lin, S.Y. Lin-Shiau, Mechanisms of hypolipidemic and anti-obesity effects of tea and tea polyphenols, Mol. Nutr. Food Res. 50 (2006) 211-217. https://doi.org/10.1002/mnfr.200500138
DOI Google Scholar
[9]

C.S. Yang, J.D. Lambert, S. Sang, Antioxidative and anti-carcinogenic activities of tea polyphenols, Arch. Toxicol. 83 (2009) 11-21. https://doi.org/10.1007/s00204-008-0372-0
DOI Google Scholar
[10]

M.E. Cavet, K.L. Harrington, T.R. Vollmer, et al., Anti-inflammatory and anti-oxidative effects of the green tea polyphenol epigallocatechin gallate in human corneal epithelial cells, Mol. Vis. 17 (2011) 533-542
Google Scholar
[11]

N. Khan, H. Mukhtar, Tea polyphenols for health promotion, Life Sci. 81 (2007) 519-533
DOI Google Scholar
[12]

J.V. Higdon, B. Frei, Tea catechins and polyphenols: health effects, metabolism, and antioxidant functions, Crit. Rev. Food Sci. Nutr. 43 (2003) 89-143. https://doi.org/10.1080/10408690390826464
DOI Google Scholar
[13]

S.S. Thanaraj, R. Seshadri, Influence of polyphenol oxidase activity and polyphenol content of tea shoot on quality of black tea, J. Sci. Food Agric. 51 (1990) 57-69. https://doi.org/10.1002/jsfa.2740510107
DOI Google Scholar
[14]

Y. Fujimura, K. Kurihara, M. Ida, et al., Metabolomics-driven nutraceutical evaluation of diverse green tea cultivars, PLoS One, 6 (2011) e23426. https://doi.org/10.1371/journal.pone.0023426
DOI Google Scholar
[15]

J. Zhou, Y. Wu, P. Long, et al., LC-MS-based metabolomics reveals the chemical changes of polyphenols during high-temperature roasting of large-leaf yellow tea, J. Agric. Food Chem. 67 (2019) 5405-5412. https://doi.org/10.1371/journal.pone.0023426
DOI Google Scholar
[16]

L.S. Lee, J.H. Choi, N. Son, et al., Metabolomic analysis of the effect of shade treatment on the nutritional and sensory qualities of green tea, J. Agric. Food Chem. 61 (2013) 332-338. https://doi.org/10.1021/jf304161y
DOI Google Scholar
[17]

C. Shao, L. Chen, C. Lu, et al., A gel-based proteomic analysis of the effects of green tea polyphenols on ovariectomized rats, Nutrition 27 (2011) 681-686. https://doi.org/10.1016/j.nut.2010.05.019
DOI Google Scholar
[18]

J. Wang, X. Bai, X. Ding, et al., Quantitative proteomic analysis reveals the role of tea polyphenol EGCG in egg whites in response to vanadium stress, Nutrition 39 (2017) 20-29. https://doi.org/10.1016/j.nut.2017.02.007
DOI Google Scholar
[19]

Q.Y. Lu, Y. Yang, Y.S. Jin, et al., Effects of green tea extract on lung cancer A549 cells: proteomic identification of proteins associated with cell migration, J. Proteom. 9 (2009) 757-767. https://doi.org/10.1002/pmic.200800019
DOI Google Scholar
[20]

X. Hao, L. Li, Y. Hu, et al., Transcriptomic analysis of the effects of three different light treatments on the biosynthesis of characteristic compounds in the tea plant by RNA-Seq, Tree Genet. Genomes. 12 (2016) 118. https://doi.org/10.1007/s11295-016-1071-2
DOI Google Scholar
[21]

R. Vittal, Z.E. Selvanayagam, Y. Sun, et al., Gene expression changes induced by green tea polyphenol (−)-epigallocatechin-3-gallate in human bronchial epithelial 21BES cells analyzed by DNA microarray, Mol. Cancer Ther. 3 (2004) 1091-1099
DOI Google Scholar
[22]

C.K. Jiang, J.Q. Ma, Z. Apostolides, et al, Metabolomics for a millenniums-old crop: tea plant (Camellia sinensis), J. Agric. Food Chem. 67 (2019) 6445-6457. https://doi.org/10.1021/acs.jafc.9b01356
DOI Google Scholar
[23]

Y. Liu, Z. Song, X. Chen, et al., Nuclear magnetic resonance-based plasma metabolomics revealed the protective effect of tea polyphenols on sulfur mustard-induced injury in rats, J. Pharm. Biomed. 186 (2020) 113278. https://doi.org/10.1016/j.jpba.2020.113278
DOI Google Scholar
[24]

J. Zhou, L. Tang, C.L. Shen, et al., Green tea polyphenols boost gut-microbiota-dependent mitochondrial TCA and urea cycles in Sprague–Dawley rats, J. Nutr. Biochem. 81 (2020) 108395. https://doi.org/10.1016/j.jnutbio.2020.108395
DOI Google Scholar
[25]

G. Chen, C. Chen, Z. Lei, Meta-omics insights in the microbial community profiling and functional characterization of fermented foods, Trends Food Sci. Technol. 65 (2017) 23-31. https://doi.org/10.1016/j.tifs.2017.05.002
DOI Google Scholar
[26]

K.J. Karczewski, M.P. Snyder, Integrative omics for health and disease, Nat. Rev. Genet. 19 (2018) 299-310. https://doi.org/10.1038/nrg.2018.4
DOI Google Scholar
[27]

Y. Hasin, M. Seldin, A. Lusis, Multi-omics approaches to disease, Genome. Biol. 18 (2017) 83. https://doi.org/10.1186/s13059-017-1215-1
DOI Google Scholar
[28]

I. Subramanian, S. Verma, S. Kumar, et al., Multi-omics data integration, interpretation, and its application, bioinform. Biol. Insights 14 (2020) 1177932219899051. https://doi.org/10.1177/1177932219899051
DOI Google Scholar
[29]

S. Huang, K. Chaudhary, L.X. Garmire, More is better: recent progress in multi-omics data integration methods, Front. Genet. 8 (2017) 84. https://doi.org/10.3389/fgene.2017.00084
DOI Google Scholar
[30]

D. Jiang, C.R. Armour, C. Hu, et al., Microbiome multi-omics network analysis: statistical considerations, limitations, and opportunities, Front. Genet. 10 (2019) 995. https://doi.org/10.3389/fgene.2019.00995
DOI Google Scholar
[31]

G. Nicora, F. Vitali, A. Dagliati, et al., Integrated multi-omics analyses in oncology: a review of machine learning methods and tools, Front. Oncol. 10 (2020) 1030. https://doi.org/10.3389/fonc.2020.01030
DOI Google Scholar
[32]

C. Wu, F. Zhou, J. Ren, et al., A selective review of multi-level omics data integration using variable selection, High Throughput 8 (2019) 4. https://doi.org/10.3390/ht8010004
DOI Google Scholar
[33]

W.W. Soon, M. Hariharan, M.P. Snyder, High-throughput sequencing for biology and medicine, Mol. Syst. Biol. 9 (2013) 640. https://doi.org/10.1038/msb.2012.61
DOI Google Scholar
[34]

Z. Wang, M. Gerstein, M. Snyder, RNA-Seq: a revolutionary tool for transcriptomics, Nat. Rev. Genet. 10 (2009) 57-63. https://doi.org/10.1038/nrg2484
DOI Google Scholar
[35]
J.M. Van Emon, Chapter 10 - Omics in fruit nutrition: concepts and application, in: A.K. Srivastava, C. Hu (Eds. ) Fruit Crops, Elsevier, 2020, pp. 121-130
DOI
[36]

T.S. Zhou, R. Zhou, Y.B. Yu, et al., Cloning and characterization of a flavonoid 3'-hydroxylase gene from tea plant (Camellia sinensis), Int. J. Mol. Sci. 17 (2016) 261. https://doi.org/10.3390/ijms17020261
DOI Google Scholar
[37]

Q. Li, S. Chai, Y. Li, et al., Biochemical components associated with microbial community shift during the pile-fermentation of primary dark tea, Front. Microbiol. 9 (2018) 1509. https://doi.org/10.3389/fmicb.2018.01509
DOI Google Scholar
[38]

W. Wang, H. Xin, M. Wang, et al., Transcriptomic analysis reveals the molecular mechanisms of drought-stress-induced decreases in Camellia sinensis leaf quality, Front. Plant Sci. 7 (2016) 385. https://doi.org/10.3389/fpls.2016.00385
DOI Google Scholar
[39]

Z.J. Wu, H.Y. Ma, J. Zhuang, iTRAQ-based proteomics monitors the withering dynamics in postharvest leaves of tea plant (Camellia sinensis), Mol. Genet. Genom. 293 (2018) 45-59. https://doi.org/10.1007/s00438-017-1362-9
DOI Google Scholar
[40]

E.H. Xia, W. Tong, Q. Wu, et al., Tea plant genomics: achievements, challenges and perspectives, Hortic. Res. 7 (2020) 7. https://doi.org/10.1038/s41438-019-0225-4
DOI Google Scholar
[41]

Y. Tai, C. Liu, S. Yu, et al., Gene co-expression network analysis reveals coordinated regulation of three characteristic secondary biosynthetic pathways in tea plant (Camellia sinensis), BMC Genom. 19 (2018) 616. https://doi.org/10.1186/s12864-018-4999-9
DOI Google Scholar
[42]

Y. Pang, I.S.B. Abeysinghe, J. He, et al., Functional characterization of proanthocyanidin pathway enzymes from tea and their application for metabolic engineering, Plant Physiol. 161 (2013) 1103-1116. https://doi.org/10.1104/pp.112.212050
DOI Google Scholar
[43]

L.Q. Zhang, K. Wei, L.Y. Wang, et al., Structure and single nucleotide polymorphism analysis of chalcone synthase genes in tea plant (Camellia sinensis), Zhongguo Nongye Kexue (Beijing, China), 47 (2014) 133-144. https://doi.org/10.1038/s41438-020-0247-y
DOI Google Scholar
[44]

S. Zhang, L. Zhang, Y. Tai, et al., Gene discovery of characteristic metabolic pathways in the tea plant (Camellia sinensis) using 'omics'-based network approaches: a future perspective, Front. Plant Sci. 9 (2018) 480. https://doi.org/10.3389/fpls.2018.00480
DOI Google Scholar
[45]

J. Handelsman, M.R. Rondon, S.F. Brady, et al., Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products, Chem. Biol. 5 (2018) R245-R249. https://doi.org/10.1016/S1074-5521(98)90108-9
DOI Google Scholar
[46]

P. Hugenholtz, G.W. Tyson, Metagenomics, Nature 455 (2008) 481-483. https://doi.org/10.1038/455481a
DOI Google Scholar
[47]

A. Knietsch, T. Waschkowitz, S. Bowien, et al., Construction and screening of metagenomic libraries derived from enrichment cultures: generation of a gene bank for genes conferring alcohol oxidoreductase activity on Escherichia coli, Appl. Environ. Microbiol. 69 (2003) 1408-1416. https://doi.org/10.1128/AEM.69.3.1408-1416.2003
DOI Google Scholar
[48]

E.E. Ilett, M. Jørgensen, M. Noguera-Julian, et al., Gut microbiome comparability of fresh-frozen versus stabilized-frozen samples from hospitalized patients using 16S rRNA gene and shotgun metagenomic sequencing, Sci. Rep. 9 (2019) 13351. https://doi.org/10.1038/s41598-019-49956-7
DOI Google Scholar
[49]

T.P. Delforno, G.V. Lacerda Júnior, M.F. Noronha, et al., Microbial diversity of a full-scale UASB reactor applied to poultry slaughterhouse wastewater treatment: integration of 16S rRNA gene amplicon and shotgun metagenomic sequencing, Microbiologyopen 6 (2017) e00443. https://doi.org/10.1002/mbo3.443
DOI Google Scholar
[50]

D. Han, P. Gao, R. Li, et al., Multicenter assessment of microbial community profiling using 16S rRNA gene sequencing and shotgun metagenomic sequencing, J. Adv. Res. 26 (2020) 111-121. https://doi.org/10.1016/j.jare.2020.07.010
DOI Google Scholar
[51]

C. Lyu, C. Chen, F. Ge, et al., A preliminary metagenomic study of puer tea during pile fermentation, J. Sci. Food Agric. 93 (2013) 3165-3174. https://doi.org/10.1002/jsfa.6149
DOI Google Scholar
[52]

H.P. Lv, Y.J. Zhang, Z. Lin, et al., Processing and chemical constituents of Pu-erh tea: a review, Food Res. Int. 53 (2013) 608-618. https://doi.org/10.1016/j.foodres.2013.02.043
DOI Google Scholar
[53]

O. Morozova, M.A. Marra, Applications of next-generation sequencing technologies in functional genomics, Genomics 92 (2008) 255-264. https://doi.org/10.1016/j.ygeno.2008.07.001
DOI Google Scholar
[54]

A. Conesa, P. Madrigal, S. Tarazona, et al., A survey of best practices for RNA-seq data analysis, Genome Biol. 17 (2016) 13. https://doi.org/10.1186/s13059-016-0881-8
DOI Google Scholar
[55]

O.D. Iancu, S. Kawane, D. Bottomly, et al., Utilizing RNA-Seq data for de novo coexpression network inference, Bioinformatics 28 (2012) 1592-1597. https://doi.org/10.1093/bioinformatics/bts245
DOI Google Scholar
[56]

L. Tierney, J. Linde, S. Müller, et al., An interspecies regulatory network inferred from simultaneous RNA-seq of Candida albicans invading innate immune cells, Front. Microbiol. 3 (2012) 85. https://doi.org/10.3389/fmicb.2012.00085
DOI Google Scholar
[57]

Y. Zhang, K. Wei, H. Li, et al., Identification of key genes involved in catechin metabolism in tea seedlings based on transcriptomic and HPLC analysis, Plant Physiol. Biochem. 133 (2018) 107-115. https://doi.org/10.1016/j.plaphy.2018.10.029
DOI Google Scholar
[58]

D. Wang, C.F. Li, C.L. Ma, et al., Novel insights into the molecular mechanisms underlying the resistance of Camellia sinensis to Ectropis oblique provided by strategic transcriptomic comparisons, Sci. Hortic. 192 (2015) 429-440. https://doi.org/10.1016/j.scienta.2015.06.005
DOI Google Scholar
[59]

Y. Yang, F. Wang, Q. Wan, et al., Transcriptome analysis using RNA-Seq revealed the effects of nitrogen form on major secondary metabolite biosynthesis in tea (Camellia sinensis) plants, Acta Physiol. Plant 40 (2018) 127. https://doi.org/10.1007/s11738-018-2701-0
DOI Google Scholar
[60]

C.Y. Shi, H. Yang, C.L. Wei, et al., Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds, BMC Genomics 12 (2011) 131. https://doi.org/10.1186/1471-2164-12-131
DOI Google Scholar
[61]

C.F. Li, Y. Zhu, Y. Yu, et al., Global transcriptome and gene regulation network for secondary metabolite biosynthesis of tea plant (Camellia sinensis), BMC Genomics 16 (2015) 560. https://doi.org/10.1186/s12864-015-1773-0
DOI Google Scholar
[62]

Z.J. Wu, X.H. Li, Z.W. Liu, et al., De novo assembly and transcriptome characterization: novel insights into catechins biosynthesis in Camellia sinensis, BMC Plant Biol. 14 (2014) 277. https://doi.org/10.1186/s12870-014-0277-4
DOI Google Scholar
[63]

Q. Xu, J. Zhu, S. Zhao, et al., Transcriptome profiling using single-molecule direct RNA sequencing approach for in-depth understanding of genes in secondary metabolism pathways of Camellia sinensis, Front. Plant Sci. 8 (2017) 1205. https://doi.org/10.3389/fpls.2017.01205
DOI Google Scholar
[64]

Y. Tai, C. Wei, H. Yang, et al., Transcriptomic and phytochemical analysis of the biosynthesis of characteristic constituents in tea (Camellia sinensis) compared with oil tea (Camellia oleifera), BMC Plant Biol. 15 (2015) 190. https://doi.org/10.1186/s12870-015-0574-6
DOI Google Scholar
[65]

A. Valdés, C. Ibáñez, C. Simó, et al., Recent transcriptomics advances and emerging applications in food science, Trends Analyt. Chem. 52 (2013) 142-154. https://doi.org/10.1016/j.trac.2013.06.014
DOI Google Scholar
[66]

C. Zhu, S. Zhang, H. Fu, et al., Transcriptome and phytochemical analyses provide new insights into long non-coding RNAs modulating characteristic secondary metabolites of Oolong tea (Camellia sinensis) in solar-withering, Front. Plant Sci. 10 (2019) 1638. https://doi.org/10.3389/fpls.2019.01638
DOI Google Scholar
[67]

H. Lee, J. Gurtowski, S. Yoo, et al., Third-generation sequencing and the future of genomics, BioRxiv (2016) 048603. https://doi.org/https://doi.org/10.1101/048603
DOI Google Scholar
[68]

F. Geraci, I. Saha, M. Bianchini, Editorial: RNA-Seq analysis: methods, applications and challenges, Front. Genet. 11 (2020) 220. https://doi.org/10.3389/fgene.2020.00220
DOI Google Scholar
[69]

R. Aebersold, M. Mann, Mass spectrometry-based proteomics, Nature 422 (2003) 198-207. https://doi.org/10.1038/nature01511
DOI Google Scholar
[70]

S. Liu, J. Gao, Z. Chen, et al., Comparative proteomics reveals the physiological differences between winter tender shoots and spring tender shoots of a novel tea (Camellia sinensis L.) cultivar evergrowing in winter, BMC Plant Biol. 17 (2017) 206. https://doi.org/10.1186/s12870-017-1144-x
DOI Google Scholar
[71]

W.C.S. Cho, Proteomics technologies and challenges, Genomics Proteomics Bioinformatics 5 (2007) 77-85. https://doi.org/10.1016/S1672-0229(07)60018-7
DOI Google Scholar
[72]

Q. Li, J. Li, S. Liu, et al., A comparative proteomic analysis of the buds and the young expanding leaves of the tea plant (Camellia sinensis L.), Int. J. Mol. Sci. 16 (2015) 14007-14038. https://doi.org/10.3390/ijms160614007
DOI Google Scholar
[73]

K. Dettmer, P.A. Aronov, B.D. Hammock, Mass spectrometry-based metabolomics, Mass Spectrom. Rev. 26 (2007) 51-78. https://doi.org/10.1002/mas.20108
DOI Google Scholar
[74]

A.M. Weljie, J. Newton, P. Mercier, et al., Targeted profiling: quantitative analysis of 1H NMR metabolomics data, Anal. Chem. 78 (2006) 4430-4442. https://doi.org/10.1021/ac060209g
DOI Google Scholar
[75]

D.S. Wishart, Quantitative metabolomics using NMR, Trends Analyt. Chem. 27 (2008) 228-237. https://doi.org/10.1016/j.trac.2007.12.001
DOI Google Scholar
[76]

H.K. Kim, Y.H. Choi, R. Verpoorte, NMR-based metabolomic analysis of plants, Nat. Protoc. 5 (2010) 536-549. https://doi.org/10.1038/nprot.2009.237
DOI Google Scholar
[77]

Q. Zhang, Y. Shi, L. Ma, et al., Metabolomic analysis using ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF MS) uncovers the effects of light intensity and temperature under shading treatments on the metabolites in tea, PLoS One 9 (2014) e112572. https://doi.org/10.1371/journal.pone.0112572
DOI Google Scholar
[78]

H.G. Ji, Y.R. Lee, M.S. Lee, et al., Diverse metabolite variations in tea (Camellia sinensis L.) leaves grown under various shade conditions revisited: a metabolomics study, J. Agric. Food Chem. 66 (2018) 1889-1897. https://doi.org/10.1021/acs.jafc.7b04768
DOI Google Scholar
[79]

Y. Zhang, Y. Wang, Z. Ding, et al., Zinc stress affects ionome and metabolome in tea plants, Plant Physiol. Biochem. 111 (2017) 318-328. https://doi.org/10.1016/j.plaphy.2016.12.014
DOI Google Scholar
[80]

J. Liu, Q. Zhang, M. Liu, et al., Metabolomic analyses reveal distinct change of metabolites and quality of green tea during the short duration of a single spring season, J. Agric. Food Chem. 64 (2016) 3302-3309. https://doi.org/10.1021/acs.jafc.6b00404
DOI Google Scholar
[81]

J. Xu, F.L. Hu, W. Wang, et al., Investigation on biochemical compositional changes during the microbial fermentation process of Fu brick tea by LC-MS based metabolomics, Food Chem. 186 (2015) 176-184. https://doi.org/10.1016/j.foodchem.2014.12.045
DOI Google Scholar
[82]

J.E. Lee, B.J. Lee, J.O. Chung, et al., 1H NMR-based metabolomic characterization during green tea (Camellia sinensis) fermentation, Int. Food Res. J. 44 (2011) 597-604. https://doi.org/10.1016/j.foodres.2010.12.004
DOI Google Scholar
[83]

L. Zhang, W.W. Deng, X.C. Wan, Advantage of LC-MS metabolomics to identify marker compounds in two types of Chinese dark tea after different post-fermentation processes, Food Sci. Biotechnol. 23 (2014) 355-360. https://doi.org/10.1007/s10068-014-0049-9
DOI Google Scholar
[84]

L. Cheng, Q. Yang, Z. Chen, et al., Distinct changes of metabolic profile and sensory quality during Qingzhuan tea processing revealed by LC-MS-based metabolomics, J. Agric. Food Chem. 68 (2020) 4955-4965. https://doi.org/10.1021/acs.jafc.0c00581
DOI Google Scholar
[85]

H. Wu, W. Huang, Z. Chen, et al., GC-MS-based metabolomic study reveals dynamic changes of chemical compositions during black tea processing, Int. Food Res. J. 120 (2019) 330-338. https://doi.org/10.1016/j.foodres.2019.02.039
DOI Google Scholar
[86]

P. Long, M. Wen, D. Granato, et al., Untargeted and targeted metabolomics reveal the chemical characteristic of Pu-erh tea (Camellia assamica) during pile-fermentation, Food Chem. 311 (2020) 125895. https://doi.org/10.1016/j.foodchem.2019.125895
DOI Google Scholar
[87]

H.G. Ji, Y.R. Lee, M.S. Lee, et al., Metabolic phenotyping of various tea (Camellia sinensis L.) cultivars and understanding of their intrinsic metabolism, Food Chem. 233 (2017) 321-330. https://doi.org/10.1016/j.foodchem.2017.04.079
DOI Google Scholar
[88]

S. Chen, J. Lin, H. Liu, et al., Insights into tissue-specific specialized metabolism in Tieguanyin tea cultivar by untargeted metabolomics, Molecules 23 (2018) 1817. https://doi.org/10.3390/molecules23071817
DOI Google Scholar
[89]

P. Li, W. Dai, M. Lu, et al., Metabolomic analysis reveals the composition differences in 13 Chinese tea cultivars of different manufacturing suitabilities, J. Sci. Food Agric. 98 (2018) 1153-1161. https://doi.org/10.1002/jsfa.8566
DOI Google Scholar
[90]

O. Fiehn, Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks, Comp. Funct. Genomics 2 (2001) 155-168. https://doi.org/10.1002/cfg.82
DOI Google Scholar
[91]

C. Zhang, M. Wang, X. Gao, et al., Multi-omics research in albino tea plants: past, present, and future, Sci. Hortic. 261 (2020) 108943. https://doi.org/10.1016/j.scienta.2019.108943
DOI Google Scholar
[92]

X. Zhao, S. Chen, S. Wang, et al., Defensive responses of tea plants (Camellia sinensis) against tea green leafhopper attack: a multi-omics study, Front. Plant Sci. 10 (2020) 1705. https://doi.org/10.3389/fpls.2019.01705
DOI Google Scholar
[93]

S.H. Chu, M. Huang, R.S. Kelly, et al., Integration of metabolomic and other omics data in population-based study designs: an epidemiological perspective, Metabolites 9 (2019) 117. https://doi.org/10.3390/metabo9060117
DOI Google Scholar
[94]

Q. Chen, J. Shi, B. Mu, et al., Metabolomics combined with proteomics provides a novel interpretation of the changes in nonvolatile compounds during white tea processing, Food Chem. 332 (2020) 127412. https://doi.org/10.1016/j.foodchem.2020.127412
DOI Google Scholar
[95]

Y. Ma, T.J. Ling, X.Q. Su, et al., Integrated proteomics and metabolomics analysis of tea leaves fermented by Aspergillus niger, Aspergillus tamarii and Aspergillus fumigatus, Food Chem. 334 (2021) 127560. https://doi.org/10.1016/j.foodchem.2020.127560
DOI Google Scholar
[96]

C. Zhou, X. Mei, D.O.N. Rothenberg, et al., Metabolome and transcriptome analysis reveals putative genes involved in anthocyanin accumulation and coloration in white and pink tea (Camellia sinensis) flower, Molecules 25 (2020) 190. https://doi.org/10.3390/molecules25010190
DOI Google Scholar
[97]

Y. Qian, S. Zhang, S. Yao, et al., Effects of vitro sucrose on quality components of tea plants (Camellia sinensis) based on transcriptomic and metabolic analysis, BMC Plant Biol. 18 (2018) 121. https://doi.org/10.1186/s12870-018-1335-0
DOI Google Scholar
[98]

G. Zhang, J. Yang, D. Cui, et al., Transcriptome and metabolic profiling unveiled roles of peroxidases in theaflavin production in black tea processing and determination of tea processing suitability, J. Agric. Food Chem. 68 (2020) 3528-3538. https://doi.org/10.1021/acs.jafc.9b07737
DOI Google Scholar
[99]

X. Yu, Y. Li, C. He, et al., Nonvolatile metabolism in postharvest tea (Camellia sinensis L.) leaves: effects of different withering treatments on nonvolatile metabolites, gene expression levels, and enzyme activity, Food Chem. 327 (2020) 126992. https://doi.org/10.1016/j.foodchem.2020.126992
DOI Google Scholar
[100]

Q. Li, J. Huang, Y. Li, et al., Fungal community succession and major components change during manufacturing process of Fu brick tea, Sci. Rep. 7 (2017) 6947. https://doi.org/10.1038/s41598-017-07098-8
DOI Google Scholar
[101]

Z. Li, C. Feng, X. Luo, et al., Revealing the influence of microbiota on the quality of Pu-erh tea during fermentation process by shotgun metagenomic and metabolomic analysis, Food Microbiol. 76 (2018) 405-415. https://doi.org/10.1016/j.fm.2018.07.001
DOI Google Scholar
[102]

S. Haider, R. Pal, Integrated analysis of transcriptomic and proteomic data, Curr. Genomics 14 (2013) 91-110. https://doi.org/10.2174/1389202911314020003
DOI Google Scholar
[103]

D. Kumar, G. Bansal, A. Narang, et al., Integrating transcriptome and proteome profiling: strategies and applications, Proteomics 16 (2016) 2533-2544. https://doi.org/10.1002/pmic.201600140
DOI Google Scholar
[104]

Q. Zhou, Z. Chen, J. Lee, et al., Proteomic analysis of tea plants (Camellia sinensis) with purple young shoots during leaf development, PLoS One 12 (2017) e0177816. https://doi.org/10.1371/journal.pone.0177816
DOI Google Scholar
[105]

S. Liu, J. Gao, Z. Chen, et al., Comparative proteomics reveals the physiological differences between winter tender shoots and spring tender shoots of a novel tea (Camellia sinensis L.) cultivar evergrowing in winter, BMC Plant Biol. 17 (2017) 206. https://doi.org/10.1186/s12870-017-1144-x
DOI Google Scholar
[106]

X. Jiang, Y. Liu, W. Li, et al., Tissue-specific, development-dependent phenolic compounds accumulation profile and gene expression pattern in tea plant (Camellia sinensis), PLoS One 8 (2013) e62315. https://doi.org/10.1371/journal.pone.0062315
DOI Google Scholar
[107]

R.M. Teng, Z.J. Wu, H.Y. Ma, et al., Differentially expressed protein are involved in dynamic changes of catechins contents in postharvest tea leaves under different temperatures, J. Agric. Food Chem. 67 (2019) 7547-7560. https://doi.org/10.1021/acs.jafc.9b01705
DOI Google Scholar
[108]

M. Zhao, D.L. Zhang, X.Q. Su, et al., An integrated metagenomics/metaproteomics investigation of the microbial communities and enzymes in solid-state fermentation of Pu-erh tea, Sci. Rep. 5 (2015) 10117. https://doi.org/10.1038/srep10117
DOI Google Scholar
[109]

M. Zhao, X.Q. Su, B. Nian, et al., Integrated meta-omics approaches to understand the microbiome of spontaneous fermentation of traditional Chinese Pu-erh tea, mSystems 4 (2019) e00680-00619. https://doi.org/10.1128/mSystems.00680-19
DOI Google Scholar
[110]

W. Wang, Y. Zhou, Y. Wu, et al., Insight into catechins metabolic pathways of Camellia sinensis based on genome and transcriptome analysis, J. Agric. Food Chem. 66 (2018) 4281-4293. https://doi.org/10.1021/acs.jafc.8b00946
DOI Google Scholar
[111]

E. Lin, H.Y. Lane, Machine learning and systems genomics approaches for multi-omics data, Biomark. Res. 5 (2017) 2. https://doi.org/10.1186/s40364-017-0082-y
DOI Google Scholar
[112]

N. Friedrich, M. Pietzner, C. Cannet, et al., Urinary metabolomics reveals glycemic and coffee associated signatures of thyroid function in two population-based cohorts, PLoS One 12 (2017) e0173078. https://doi.org/10.1371/journal.pone.0173078
DOI Google Scholar
[113]

F.T. Zhang, Z.H. Zhu, X.R. Tong, et al., Mixed linear model approaches of association mapping for complex traits absed on omics variants, Sci. Rep. 5 (2015) 10298. https://doi.org/10.1038/srep10298
DOI Google Scholar
[114]

I. De Toma, M. Ortega, S. Catuara-Solarz, et al., Re-establishment of the epigenetic state and rescue of kinome deregulation in Ts65Dn mice upon treatment with green tea extract and environmental enrichment, Sci. Rep. 10 (2020) 16023. https://doi.org/10.1038/s41598-020-72625-z
DOI Google Scholar
[115]

Q. Shen, L. Fu, F. Dai, et al., Multi-omics analysis reveals molecular mechanisms of shoot adaption to salt stress in Tibetan wild barley, BMC Genomics 17 (2016) 889. https://doi.org/10.1186/s12864-016-3242-9
DOI Google Scholar
[116]

M.S. Clark, U. Sommer, J.K. Sihra, et al., Biodiversity in marine invertebrate responses to acute warming revealed by a comparative multi-omics approach, Glob. Chang. Biol. 23 (2017) 318-330. https://doi.org/10.1111/gcb.13357
DOI Google Scholar
[117]

E. Saccenti, M.E. Timmerman, Approaches to sample size determination for multivariate data: applications to PCA and PLS-DA of omics data, J. Proteome Res. 15 (2016) 2379-2393. https://doi.org/10.1021/acs.jproteome.5b01029
DOI Google Scholar
[118]

L. Khorraminezhad, M. Leclercq, A. Droit, et al., Statistical and machine-learning analyses in nutritional genomics studies, Nutrients 12 (2020) 3140. https://doi.org/10.3390/nu12103140
DOI Google Scholar
[119]

K.M. Mendez, D.I. Broadhurst, S.N. Reinke, Migrating from partial least squares discriminant analysis to artificial neural networks: a comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks, Metabolomics 16 (2020) 17. https://doi.org/10.1007/s11306-020-1640-0
DOI Google Scholar
[120]

K.M. Mendez, S.N. Reinke, D.I. Broadhurst, A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification, Metabolomics 15 (2019) 150. https://doi.org/10.1007/s11306-019-1612-4
DOI Google Scholar
[121]

H. Zhu, F. Liu, Y. Ye, et al., Application of machine learning algorithms in quality assurance of fermentation process of black tea – based on electrical properties, J. Food Eng. 263 (2019) 165-172. https://doi.org/10.1016/j.jfoodeng.2019.06.009
DOI Google Scholar
[122]

W. Lin, M. Lin, H. Zhou, et al., The effects of chemical and organic fertilizer usage on rhizosphere soil in tea orchards, PLoS One 14 (2019) e0217018. https://doi.org/10.1371/journal.pone.0217018
DOI Google Scholar
[123]

T. Tounekti, E. Joubert, I. Hernández, et al., Improving the polyphenol content of tea, CRC Crit. Rev. Plant Sci. 32 (2013) 192-215. https://doi.org/10.1080/07352689.2012.747384
DOI Google Scholar
[124]

F.S. Nathoo, L. Kong, H. Zhu, et al., A review of statistical methods in imaging genetics, Can. J. Stat. 47 (2019) 108-131. https://doi.org/10.1002/cjs.11487
DOI Google Scholar
[125]

M. Shaheenuzzamn, T. Liu, S. Shi, et al., Development of sequencing technology and the role of next generation senquencing technologies in wheat research: a review, Pak. J. Bot. 52 (2020) 1867-1878. https://doi.org/10.30848/PJB2020-5(33)
DOI Google Scholar
[126]

D. Liu, S. Yang, K. Kavdia, et al., Deep profiling of microgram-scale proteome by tandem mass tag mass spectrometry, J. Proteome Res. (2020). https://doi.org/10.1021/acs.jproteome.0c00426
DOI Google Scholar
[127]

T. Cajka, O. Fiehn, Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics, Anal. Chem. 88 (2016) 524-545. https://doi.org/10.1021/acs.analchem.5b04491
DOI Google Scholar
[128]

F. Zheng, X. Zhao, Z. Zeng, et al., Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography–mass spectrometry, Nat. Protoc. 15 (2020) 2519-2537. https://doi.org/10.1038/s41596-020-0341-5
DOI Google Scholar
[129]

Y. Li, M. Kuhn, A.C. Gavin, et al., Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features, Bioinformatics 36 (2020) 1213-1218. https://doi.org/10.1093/bioinformatics/btz736
DOI Google Scholar
[130]

P. Bonini, T. Kind, H. Tsugawa, et al., Retip: retention time prediction for compound annotation in untargeted metabolomics, Anal. Chem. 92 (2020) 7515-7522. https://doi.org/10.1021/acs.analchem.9b05765
DOI Google Scholar
[131]

E.H. Mahood, L.H. Kruse, G.D. Moghe, Machine learning: a powerful tool for gene function prediction in plants, Appl. Plant Sci. 8 (2020) e11376. https://doi.org/10.1002/aps3.11376
DOI Google Scholar
[132]

K. Dührkop, L.F. Nothias, M. Fleischauer, et al., Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra, Nat. Biotechnol. 39 (2021) 462–471. https://doi.org/10.1038/s41587-020-0740-8
DOI Google Scholar
[133]

G. Hong, J. Wang, Y. Zhang, et al., Biosynthesis of catechin components is differentially regulated in dark-treated tea (Camellia sinensis L.), Plant Physiol. Biochem. 78 (2014) 49-52. https://doi.org/10.1016/j.plaphy.2014.02.017
DOI Google Scholar
[134]

M. Takemoto, H. Takemoto, Synthesis of theaflavins and their functions. Molecules 23 (2018) 918. https://doi.org/10.3390/molecules23040918
DOI Google Scholar
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Received: 31 December 2020
Revised: 08 February 2021
Accepted: 09 February 2021
Published: 04 February 2022
Issue date: May 2022

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© 2022 Beijing Academy of Food Sciences.

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